{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "46a06471",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79dbfda",
   "metadata": {},
   "source": [
    "load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "7d238e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0   1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1   1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2   1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3   1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4   1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "\n",
       "   男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
       "0      0  2785      62  170  72.599998  25.120001  \n",
       "1     60  3133      68  174  52.700001  17.410000  \n",
       "2      0  3901      80  169  46.500000  16.280001  \n",
       "3      0  4946     100  183  79.699997  23.799999  \n",
       "4     68  3538      74  171  54.700001  18.709999  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_boys = pd.read_excel('./体测分数_男生.xls') \n",
    "data_boys.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "18cc9d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0   1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1   1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2   1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3   1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4   1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "\n",
       "   女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0     85  3775     100  163.0  51.299999  19.309999  \n",
       "1     66  3683     100  163.0  66.599998  25.070000  \n",
       "2     76  3331     100  157.0  60.000000  24.340000  \n",
       "3     85  3701     100  160.0  50.700001  19.799999  \n",
       "4     70  3592     100  167.0  63.900002  22.910000  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_girls = pd.read_excel('./体测分数_女生.xls') \n",
    "data_girls.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f1043e0",
   "metadata": {},
   "source": [
    "## 对男1000米跑、男引体进行等宽分箱操作，分成3份，并使用饼图绘制百分比"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1dad713",
   "metadata": {},
   "source": [
    "boy's 1000m run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0c223b90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data_boys['男1000米跑']\n",
    "#取非0数据\n",
    "cond = df.map(lambda x : x >0) \n",
    "df = df[cond]\n",
    "p = pd.cut(df,bins=3,labels=['1','2','3'],right = True).value_counts()\n",
    "labels = ['快','中','慢']\n",
    "_ = plt.pie(p,labels = labels ,\n",
    "        textprops = {'family':'Kaiti', 'fontsize':16},\n",
    "        autopct = '%0.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65118c27",
   "metadata": {},
   "source": [
    "Boy's pull-ups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "dd9f186a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data_boys['男引体']\n",
    "#取非0数据\n",
    "cond = df.map(lambda x : x >0) \n",
    "df = df[cond]\n",
    "p = pd.cut(df,bins=3,labels=['1','2','3'],right = True).value_counts()\n",
    "labels = ['少','中','多']\n",
    "_ = plt.pie(p,labels = labels ,\n",
    "        textprops = {'family':'Kaiti', 'fontsize':16},\n",
    "        autopct = '%0.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05edc661",
   "metadata": {},
   "source": [
    "## 对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c5ae63",
   "metadata": {},
   "source": [
    "女800米跑各分数段人数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0d2c83e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data_girls['女800米跑分数'] \n",
    "df.plot.hist(bins = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c19c3f",
   "metadata": {},
   "source": [
    "女跳远各分数段人数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e4f8491c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data_girls['女跳远分数'] \n",
    "df.plot.hist(bins = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0eaa46b",
   "metadata": {},
   "source": [
    "## 使用嵌套饼图对比男女生体重指数进行比例统计，分为正常、低体重、超重、肥胖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "52211433",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>体重指数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "      <td>低体重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0   1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1   1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2   1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3   1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4   1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "\n",
       "   男引体分数  男肺活量  男肺活量分数   身高         体重        BMI 体重指数  \n",
       "0      0  2785      62  170  72.599998  25.120001   超重  \n",
       "1     60  3133      68  174  52.700001  17.410000   正常  \n",
       "2      0  3901      80  169  46.500000  16.280001  低体重  \n",
       "3      0  4946     100  183  79.699997  23.799999   超重  \n",
       "4     68  3538      74  171  54.700001  18.709999   正常  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取非零数据\n",
    "df = data_boys['BMI']\n",
    "cond = df.map(lambda x : x >0) \n",
    "data_boys = data_boys[cond]\n",
    "\n",
    "def convert(x):\n",
    "    if x >= 26.4:\n",
    "        return '肥胖'\n",
    "    if 23.3<= x <26.3:\n",
    "        return '超重'\n",
    "    if x <= 16.4:\n",
    "        return '低体重'\n",
    "    return '正常'\n",
    "\n",
    "data_boys['体重指数'] = data_boys['BMI'].apply(convert)\n",
    "data_boys.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "290c1078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>体重指数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0   1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1   1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2   1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3   1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4   1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "\n",
       "   女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI 体重指数  \n",
       "0     85  3775     100  163.0  51.299999  19.309999   正常  \n",
       "1     66  3683     100  163.0  66.599998  25.070000   超重  \n",
       "2     76  3331     100  157.0  60.000000  24.340000   超重  \n",
       "3     85  3701     100  160.0  50.700001  19.799999   正常  \n",
       "4     70  3592     100  167.0  63.900002  22.910000   超重  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取非零数据\n",
    "df = data_girls['BMI']\n",
    "cond = df.map(lambda x : x >0) \n",
    "data_girls = data_girls[cond]\n",
    "\n",
    "def convert(x):\n",
    "    if x >= 25.3:\n",
    "        return '肥胖'\n",
    "    if 22.8<= x <25.2:\n",
    "        return '超重'\n",
    "    if x <= 16.4:\n",
    "        return '低体重'\n",
    "    return '正常'\n",
    "\n",
    "data_girls['体重指数'] = data_girls['BMI'].apply(convert)\n",
    "data_girls.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "f610e7aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8)) \n",
    "p = [data_boys.shape[0],data_girls.shape[0]]\n",
    "labels = ['男','女']\n",
    "_ = plt.pie(p,radius=0.6,labels = labels ,\n",
    "        textprops = {'family':'Kaiti', 'fontsize':16},\n",
    "        autopct = '%0.2f%%')\n",
    "\n",
    "p1 = data_boys['体重指数'].value_counts()\n",
    "p2 = data_girls['体重指数'].value_counts()\n",
    "p3 = p1.append(p2)\n",
    "labels = p3.index.values\n",
    "\n",
    "_ = plt.pie(p3,radius=1.1,labels = labels,\n",
    "        textprops = {'family':'Kaiti', 'fontsize':16},\n",
    "        autopct = '%0.2f%%',wedgeprops = {'linewidth':2,\n",
    "                    'width':0.3,\n",
    "                    'edgecolor':'white'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20386e01",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
